The total flavonoids from sea buckthorn (TFSB) exhibit a potent anti-inflammatory activity; however, the effect of TFSB on respiratory inflammatory disease is not fully known. The present study evaluated the potential of TFSB to prevent airway inflammation and the underlying mechanism. The results showed that TFSB remarkably inhibited lipopolysaccharide/cigarette smoke extract (LPS/CSE)-induced expression of IL-1β, IL-6, CXCL1, and MUC5AC at both mRNA and protein levels in HBE16 bronchial epithelial cells. TFSB also decreased the production of PGE 2 through inhibition the expression of COX2 in LPS/CSE-stimulated HBE16 cells. Furthermore, bronchoalveolar fluid and histological analyses revealed that LPS/cigarette smoke exposure-induced elevated cell numbers of neutrophils and macrophages in bronchoalveolar fluid, inflammatory cell infiltration, and airway remodeling were remarkably attenuated by TFSB in mice. Immunohistochemical results also confirmed that TFSB decreased the expression of IL-1β, IL-6, COX2, CXCL1, and MUC5AC in LPS/CSexposed mice. Mechanistically, TFSB blocked LPS/CSE-induced activation of ERK, Akt, and PKCα. Molecular docking further confirmed that the main components in TFSB including quercetin and isorhamnetin showed potent binding affinities to MAPK1 and PIK3CG, two upstream kinases of ERK and Akt, respectively. In summary, TFSB exerts a potent protective effect against LPS/CS-induced airway inflammation through inhibition of ERK, PI3K/Akt, and PKCα pathways, suggesting that TFSB may be a novel therapeutic agent for respiratory diseases. KEYWORDS chronic bronchitis, ERK, PI3K/Akt, and PKC pathways, lipopolysaccharide/cigarette smoke, sea buckthorn, total flavonoids

Purpose: To study the mechanism involved in the anti-cholecystitis effect the Tibetan medicine “Dida”, using network pharmacology-integrated molecular docking simulationsMethods: In this investigation, the bioactive compounds of Dida were collected, network pharmacology methods to predict their targets, and networks were constructed through GO and KEGG pathway analyses. The potential binding between the bioactive compounds and the targets were demonstrated using molecular docking simulations.Results: A total of 12 bioactive compounds and 50 key targets of Dida were identified. Two networks, namely, protein-protein interaction (PPI) network of cholecystitis targets, and compound-target-pathway network, were established. Network analysis showed that 10 targets (GAPDH, AKT1, CASP3, EGFR, TNF, MAPK3, MAPK1, HSP90AA1, STAT3, and BCL2L1) may be the therapeutic targets of Dida in cholecystitis. Analysis of the KEGG pathway indicated that the anti-cholecystitis effect of Dida may its regulation of a few crucial pathways, such as apoptosis, as well as toll-like  receptor, T cell receptor, NOD-like receptor, and MAPK signaling pathways. Furthermore, molecular docking simulation revealed that CASP3, CAPDH, HSP90AA1, MAPK3, MAPK1, and STAT3 had well-characterized interactions with the corresponding compounds.Conclusion: The mechanism underlying the anti-cholecystitis effect of Dida was successfully predicted and verified using a combination of network pharmacology and molecular docking simulation. This provides a firm basis for the experimental verification of the use of Dida in the treatment of cholecystitis, and enhances its rational application in clinical medication. Keywords: Tibetan medicine, Dida, Cholecystitis, Mechanism of effect, Network pharmacology, Molecular docking simulation

Background: Ferroptosis-related genes (FRGs) play vital roles in survival and prognosis of prostate cancer (PCa) patients. We establish a ferroptosis-related prediction model through bioinformatics analysis for overall survival (OS) and disease-free survival (DFS), so as to evaluate the clinical survival status through the characteristics of immune cell infiltration (ICI), which could provide information for treatment monitoring.Methods: At first, 268 FRGs were obtained from previous studies. Differentially expressed FRGs were identified based on The Cancer Genome Atlas (TCGA) database, and FRG enrichment analysis was performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We then performed univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses to establish OS-and DFS-related prognostic prediction models. The association of the model and clinicopathological features was further analyzed. Subsequently, unique genomic signatures of immune cell subsets were obtained through the KEGG database. Based on specific genes associated with ferroptosis and their association with ICI, immune infiltration was assessed in patients in different risk groups.Results: We constructed an OS-and an DFS-prognostic model through bioinformatics analysis. The predicted values of OS and DFS-related models were higher in T3-4 than in T1-2 (P=0.0057, P<0.001), and the predicted value of the DFS model in N0 stage was higher than that in N1 stage (P=0.0136). Results of Single-sample gene set enrichment analysis (ssGSEA) on the basis of the KEGG dataset showed p53 signaling being the most enriched signal in the high-risk group, while endocytosis was the most enriched signal in the low-risk group. M2 macrophages (P=0.007) and neutrophils (P=0.024) were enriched in the high-risk group, and CD4-activated memory T cells were significantly accumulated in the low-risk group (P=0.017). Conclusions:The OS-and DFS-related model based on FRGs and ICI create new insights into the disease state assessment of PCa patients., which may aid in the development of individualized and precise treatment in the future.

ObjectiveIncreasing evidence suggests that gut microbiota is involved in the occurrence and progression of urinary system diseases such as clear cell renal cell carcinoma (ccRCC). However, the mechanism of how alteration of gut metagenome promotes ccRCC remains unclear. Here we aim to elucidate the association of specific gut bacteria and their metabolites with ccRCC.MethodsIn a pilot case-control study among 30 ccRCC patients (RCC group) and 30 healthy controls (Control group), 16S ribosomal RNA (rRNA) gene sequencing were analyzed from fecal samples collected prior to surgery or hospitalization. Alpha diversity and beta diversity analysis of the gut microbiota were performed, and differential taxa were identified by multivariate statistics. Meanwhile, serum metabolism was measured by UHPLC-MS, and differential genes were identified based on the TCGA database.ResultsAlpha diversity found there were no significant microbial diversity differences of gut microbiota between the RCC group and the Control group. However, beta diversity analysis showed that the overall structures of the two groups were significantly separated (p = 0.008). Random Forests revealed the relative abundances of 20 species differed significantly between the RCC group and the Control group, among which nine species were enriched in the RCC group such as Desulfovibrionaceae, and 11 species were less abundant such as four kinds of Lactobacillus. Concomitantly, serum level of taurine, which was considered to be consumed by Desulfovibrionaceae and released by Lactobacillus, has decreased in the RCC group. In addition, macrophage-related genes such as Gabbr1 was upregulated in ccRCC patients.ConclusionReduction of protective bacteria, proliferation of sulfide-degrading bacteria Desulfovibrionaceae, reduction of taurine, and enrichment of macrophage related genes might be the risk predictors of ccRCC.